Mathematical rules for synergistic,additive,and antagonistic effects of multi-drug combinations and their application in research and development of combinatorial drugs and special medical food combinations

Multi-drug(or multi-element)combinations are often prescribed in the practice of clinical medicine and as foods for special medical purposes.The main motivations for these combinations are that most diseases contain multiple related targets and an appropriate combination can maximize benefits while minimizing adverse reactions.As such,it is especially important to derive mathematical models for their quantitative calculation.In this paper,we introduce mathematical rules for the synergistic,additive,and antagonistic effects of multi-drug combinations developed in our laboratory.We have established a“onebelt,one-line”model and provide examples of the quantitative calculation of the synergistic,additive,and antagonistic effects of a combination of multiple components.We also explain how to scientifically and precisely determine the intensity of these synergies,additions and antagonisms,as well as their corresponding dose ranges,thereby laying a solid theoretical foundation for market listing combinatorial drugs and foods for special medical purposes.

Comparison of Dextran Perfusion and GSI-B4 Isolectin Staining in a Mouse Model of Oxygen-induced Retinopathy

Purpose: Oxygen-induced retinopathy(OIR) is a robust and widely used animal model for the study of retinal neovascularization(NV). Dextran perfusion and Griffonia simplicifolia isolectin B4(GSI-B4) staining are two common methods for examining the occurrence and extent of OIR. This study provides a quantitative comparison of the two for OIR detection.Methods: At postnatal day 7(PN7), fifteen C57 BL / 6J mice were exposed to a 75% hyperoxic condition for 5 days and then returned to room air conditions. At PN17, the mice received intravitreal injection of GSI-B4 Alexa Fluor 568 conjugate. After 10 hours, they were infused with FITC-dextran conjugate via the left ventricle. Retinal flat mounts were photographed by confocal microscopy. Areas with fluorescent signals and the total retinal areas were quantified by Image J software.Results:Both GSI-B4 and dextran detected the peripheral neovascular area. The mean hyper fluorescence area was 0.33 ±0.14% of whole retinal area determined by GSI-B4 staining and 0.25±0.28% determined by dextran perfusion. The difference between the two measures was 0.08%(95% CI:-0.59%,0.43%)..The Pearson correlation coefficient between the two methods was 0.386,P =0.035..The mean coincidence rates were 14.3 ±13.4% and 24.9 ±18.5% for GSI-B4 and dextran staining, respectively.Conclusion:.Both methods can complement each other indemonstrating and quantitatively evaluating retinal NV. A poor agreement was found between the two methods;.GSI-B4 isolectin was more effective than FITC-dextran perfusion in evaluating the extent of retinal NV in a mouse model of OIR.

An artificial intelligence approach for particle transport velocity prediction in horizontal flows

Particle entrainment is an inevitable phenomenon in pipeline systems,especially during the development and extraction phases of oil and gas wells.Accurately predicting the critical velocity for particle transport is a key focus for implementing effective sand control management.This work presents a semi-supervised learning–deep hybrid kernel extreme learning machine(SSL-DHKELM)model for predicting the critical velocity,which integrates multiple machine learning theories including the deep learning approach,which is adept at advanced feature extraction.Meanwhile,the SSL framework enhances the model's capabilities when data availability is limited.An improved slime mould algorithm is also employed to optimize the model's hyperparameters.The proposed model has high accuracy on both the sample dataset and out-of-sample data.When trained with only 10%of the data,the model's error still did not increase significantly.Additionally,this model achieves superior predictive accuracy compared to existing mechanistic models,demonstrating its impressive performance and robustness.

Deep learning-based optical aberration estimation enables offline digital adaptive optics and super-resolution imaging

Optical aberrations degrade the performance of fluorescence microscopy.Conventional adaptive optics(AO)leverages specific devices,such as the Shack–Hartmann wavefront sensor and deformable mirror,to measure and correct optical aberrations.However,conventional AO requires either additional hardware or a more complicated imaging procedure,resulting in higher cost or a lower acquisition speed.In this study,we proposed a novel space-frequency encoding network(SFE-Net)that can directly estimate the aberrated point spread functions(PSFs)from biological images,enabling fast optical aberration estimation with high accuracy without engaging extra optics and image acquisition.We showed that with the estimated PSFs,the optical aberration can be computationally removed by the deconvolution algorithm.Furthermore,to fully exploit the benefits of SFE-Net,we incorporated the estimated PSF with neural network architecture design to devise an aberration-aware deeplearning super-resolution model,dubbed SFT-DFCAN.We demonstrated that the combination of SFE-Net and SFT-DFCAN enables instant digital AO and optical aberration-aware super-resolution reconstruction for live-cell imaging.

Mechanical property and cellular structure of an additive manufactured FeCoNiCrMo_(0.2) high-entropy alloy at high-velocity deformation

The microstructural modification for cellular structures can achieve the improvement of dynamic me-chanical properties of a selective laser melted FeCoNiCrMo_(0.2)high-entropy alloy(SLM-FeCoNiCrMo_(0.2)HEA)and can expand its promising applications in the field of high-velocity deformation.In this work,FeCoNiCrMo_(0.2)HEAs with cellular structures in different sizes were produced by selective laser melt-ing(SLM)with different process parameters.The dynamic mechanical properties and microstructure of the SLM-FeCoNiCrMo_(0.2)HEA were studied.The dynamic mechanical properties of the SLM-FeCoNiCrMo_(0.2)HEA increased with decrease of average size of cellular structures,and the values of them were sensitive to strain rates.The energy absorption,compressive strength and yield strength of the SLM-FeCoNiCrMo_(0.2)HEAs reached 315.6 MJ/m^(3),2.2 GPa and 775.6 MPa,respectively at a strain rate of 2,420 s^(−1),under the process parameters of laser power and scanning speed of 330 W and 800 mm/s,respectively,where the corresponding average size of cellular structures in the HEAs was 483.6 nm.The value of strain-hardening rate of the SLM-FeCoNiCrMo_(0.2)HEA was about 5.1 GPa at a strain level of 0.1,which was much higher than that of the powder-metallurgy FeCoNiCrMo_(0.2)HEA.The cellular structure was formed inside the molten pool with segregation of Mo on the boundary.Deformation localization appeared in the cellular structures,forming several deformation bands after high strain-rate deformation.The elemental segre-gation strengthening and dislocation strengthening are considered to be the main strengthening mecha-nisms in SLM-FeCoNiCrMo_(0.2)HEA.

A perceptual quality metric for 3D triangle meshes based on spatial pooling

In computer graphics, various processing operations are applied to 3D triangle meshes and these processes often involve distortions, which affect the visual quality of surface geometry. In this context, perceptual quality assessment of 3D triangle meshes has become a crucial issue. In this paper, we propose a new objective quality metric for assessing the visual difference between a reference mesh and a corresponding distorted mesh. Our analysis indicates that the overall quality of a distorted mesh is sensitive to the distortion distribution. The proposed metric is based on a spatial pooling strategy and statistical descriptors of the dis- tortion distribution. We generate a perceptual distortion map for vertices in the reference mesh while taking into account the visual masking effect of the human visual system. The proposed metric extracts statistical descriptors from the dis- tortion map as the feature vector to represent the overall mesh quality. With the feature vector as input, we adopt a support vector regression model to predict the mesh quality score. We validate the performance of our method with three publicly available databases, and the comparison with state-of-the-art metrics demonstrates the superiority of our method. Experimental results show that our proposed method achieves a high correlation between objective assessment and subjective scores.

Fabrication of 3D ordered needle-like polyaniline@hollow carbon nanofibers composites for flexible supercapacitors

Carbon nanofiber-based supercapacitors have broad prospects in powering wearable electronics owing to their high specific capacity,fast charge/discharge process,along with long-cycling life.Herein,a poly(ac rylo n it rile-co-β-methyl hydrogen itaconate) copolymer was prepared and used to synthesize flexible hollow carbon nanofibers(HCNFs) via an electrospinning method without breaking after multiple bending.Subsequently,the inner and outer surfaces of HCNFs were evenly covered with ordered needlelike polyaniline(PANI) through in-situ polymerization methods to obtain three-dimensional flexible HCNFs/PANI composites,which exhibited a high capacity 1196.7 F/g at 1 A/g and good cycling stability(90.1% retention at 5 A/g after 3000 cycles).The symmetrical supercapacitor based on the HCNFs/PANI composites also delive red an outsta nding electrochemical performance with high energy/power density(60.28 Wh/kg at 1000 W/kg) and superior cycling durability(90% capacitance retention after at 5 A/g3000 cycles),which confirmed that the HCNFs/PANI composites had a wide application potential in flexible energy storage devices.

Building better solid-state batteries with silicon-based anodes

Silicon(Si)-based solid-state batteries(Si-SSBs)are attracting tremendous attention because of their high energy density and unprecedented safety,making them become promising candidates for next-generation energy storage systems.Nevertheless,the commercialization of Si-SSBs is significantly impeded by enormous challenges including large volume variation,severe interfacial problems,elusive fundamental mechanisms,and unsatisfied electrochemical performance.Besides,some unknown electrochemical processes in Si-based anode,solid-state electrolytes(SSEs),and Si-based anode/SSE interfaces are still needed to be explored,while an in-depth understanding of solid–solid interfacial chemistry is insufficient in Si-SSBs.This review aims to summarize the current scientific and technological advances and insights into tackling challenges to promote the deployment of Si-SSBs.First,the differences between various conventional liquid electrolyte-dominated Si-based lithium-ion batteries(LIBs)with Si-SSBs are discussed.Subsequently,the interfacial mechanical contact model,chemical reaction properties,and charge transfer kinetics(mechanical–chemical kinetics)between Si-based anode and three different SSEs(inorganic(oxides)SSEs,organic–inorganic composite SSEs,and inorganic(sulfides)SSEs)are systemically reviewed,respectively.Moreover,the progress for promising inorganic(sulfides)SSE-based Si-SSBs on the aspects of electrode constitution,three-dimensional structured electrodes,and external stack pressure is highlighted,respectively.Finally,future research directions and prospects in the development of Si-SSBs are proposed.

High performance flexible Sn-Pb mixed perovskite solar cells enabled by a crosslinking additive

Flexible perovskite solar cells(PSCs)have drawn increasing attention due to their promising applications for wearable electronics and aerospace applications.However,the efficiency and stability of flexible PSCs still lag behind their rigid counterparts.Here,we use N,N-dimethyl acrylamide(DMAA)to in situ synthesize cross-linking polymer for flexible Sn–Pb mixed PSCs.DMAA can gather at grain boundary as a scaffold to regulate the crystallization of perovskite and reduce defects.The rigid and flexible Sn–Pb mixed PSCs showed efficiencies of 16.44%and 15.44%,respectively.In addition,the flexible Sn–Pb mixed PSCs demonstrated excellent bending durability,which retained over 80%of the original efficiency after 5000 bending cycles at a radius of 5 mm.